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About This Role
The Oracle Cloud Infrastructure Data Science and Analytics team is seeking a passionate, experienced Senior Data Scientist to tackle complex technical challenges of the Infrastructure Build processes, building sophisticated analytical solutions to drive optimization and efficiency. The candidate should have deep expertise in researching disparate data sources, setting up large\-scale data pipelines, and deploying large language models in OCI's growing Infrastructure space. As OCI continues to grow the customer base and regions, we need hands\-on scientists who can lead data\-driven initiatives to ensure that OCI's Infrastructure is in\-tune to support new regions/ expansions. The ideal candidate is someone who thrives in the face of ambiguity and can quickly distill abstract ideas into concrete solutions and has a proven track record of delivering data\-driven solutions across a highly complex environment involving multiple organizations, departments and teams.
The Senior Data Scientist will be responsible for statistical analyses, ML/ large language model deployment, forecasting models and lead business intelligence initiatives that impact Infrastructure and Networking for OCI. In addition, this specific role requires a passion for solving high\-impact problems and the ability to jump into any in\-flight project and get it on the rails.
RESPONSIBILITIES
What You’ll Do
- Lead exploratory research and rigorous statistical analysis to translate complex data into clear, actionable answers to strategic business questions; apply data transformation, experimental design, predictive modeling, and machine\-learning methods as appropriate.
- Design, fine\-tune, and optimize scalable algorithms and models, ensuring strong reliability, performance, reproducibility, and operational readiness in high\-volume production environments.
- Partner with data scientists, engineers, product teams, and business stakeholders to define data requirements, evaluate analytical opportunities, and deliver solutions aligned with measurable business outcomes.
- Establish and champion data\-quality standards, including validation, monitoring, lineage, and remediation practices, recognizing that reliable inputs are foundational to trustworthy analytical and ML outputs.
- Mentor junior data scientists on statistical rigor, modeling best practices, experiment evaluation, code quality, and effective communication of data\-driven findings.
- Lead the development, evaluation, and fine\-tuning of LLM\-based solutions for infrastructure\-build and data\-center\-materials use cases, including domain adaptation, retrieval/evaluation strategies, and performance measurement.
- Stay current on advances in statistics, machine learning, and data science; assess and apply emerging methods to improve business processes, product capabilities, and decision quality.
- Stay up\-to\-date with the latest developments in machine learning, statistics, and data science, applying new techniques for process/ product improvements.
Minimum Qualifications
- Bachelor’s degree, or equivalent practical experience, in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, Economics, or a related quantitative field.
- Strong foundation in probability, statistical inference, experimental design, hypothesis testing, regression, and predictive modeling.
- Experience applying rigorous statistical analysis to large, complex datasets to answer business questions, quantify uncertainty, and communicate actionable insights.
- Proficiency in SQL and Python or R for data extraction, transformation, analysis, visualization, and reproducible modeling workflows.
- Experience with data warehousing and working with structured and semi\-structured data at scale.
- Demonstrated ability to communicate analytical findings clearly to both technical and non\-technical stakeholders.
- Hands\-on experience developing, validating, and deploying machine\-learning models, including model selection, feature engineering, performance evaluation, and monitoring.
- Experience evaluating and adapting LLM or generative\-AI solutions for business use cases, including prompt design, benchmarking, and quality assessment.
- Collaborative problem\-solving skills and the ability to develop, evaluate, and iterate on analytical solutions in an ambiguous environment.
Preferred Qualifications (Nice To Have)
- Experience with cloud infrastructure and networking domain
- Experience with MLOps, building workflows for model retraining, monitoring and deploying
- Experience working with ambiguous problem and driving it to the finish line
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Oracle, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Oracle AI Hiring
Oracle has 17 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Agent Developer, Research Scientist. Positions span US, Nashville, TN, US, Santa Clara, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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